Model for predicting distress on a component

1. A method of predicting distress on a physical component, the method comprising: receiving, via one or more sensors communicatively coupled to the physical component, a plurality of data associated with the physical component; transferring observed distress data of the plurality of data associated with the physical component, to a data controller, via the one or more sensors being communicatively coupled to the data controller, and obtaining from a distress rank module, a distress rank output value being associated with the observed distress data; generating, via a predictive module, a predictive model representative of a failure of the physical component based on parameters including at least one of environmental data, operational data and modeling data associated with the physical component, each of the at least one environmental data, operational data and modeling data being obtained by the predictive module; wherein the predictive model provides a distress output value; comparing, via a comparative module, the distress output value to the distress rank output value to output a mathematical difference, the distressed output value being scaled before being compared to the distress rank output value; and communicating the mathematical difference to the predictive module; and tuning, via the predictive module, the predictive model by adjusting at least one parameter of the parameters and generating an updated predictive model, the updated predictive model providing an updated distress output value, the updated distressed output value being generated as a computational model; wherein the updated predictive model is generated when the mathematical difference fails to satisfy a predetermined threshold; and predicting distress of the physical component responsive to one of the predictive model and the updated predictive model.

2. The method of predicting of claim 1 , wherein the updated predictive model provides an updated distress output value; comparing, via the comparative module, the updated distress output value to the distress rank output value to output an updated mathematical difference; and communicating the updated mathematical difference to the predictive module and tuning, via predictive module, another updated predictive model.

3. The method of claim 1 , wherein the observed distress data includes a first set of distress data that includes 20 data points or more.

4. The method of claim 3 , further including obtaining a second set of distress data wherein tuning the predictive model is based on one of the first set of distress data or the second set of distress data.

5. The method of claim 4 , wherein the second set of distress data includes 5 data points or more.

6. The method of claim 1 , further including preparing a display illustrating the predicted distress on the physical component.

7. The method of claim 6 , wherein the display is a graph.

8. The method of claim 1 , wherein the physical component is a component of an aircraft.

9. The method of claim 1 , wherein the at least one parameter is one of operation parameters of the physical component, design specific parameters specific to the physical component, or environmental parameters of the physical component.

10. The method of claim 3 , wherein the first set of distress data is based in part on physical inspection of the physical component.

11. The method of claim 1 , wherein performing the method is part of modifying a design change of the physical component.

12. The method of claim 1 , wherein performing the method is part of developing an inspection schedule of the physical component.

13. The method of claim 1 , wherein the method further comprises restarting the method by obtaining new observed distress data.

14. The method of claim 1 , wherein the method further comprises using a set of design specific data related to the physical component as a parameter of the parameters.

15. The method of claim 1 , wherein tuning the predictive model includes reformulating the parameters.

16. The method of claim 15 , wherein the reformulating the parameters is based on a different cumulative damage model of the modeling data.

17. A system for predicting distress on a physical component, the system comprising: a controller coupled to a memory configured for storing observed distress data, environmental data, operational data and modeling data related to a physical component; one or more sensors being (i) communicatively coupled to the controller and the physical component and (ii) configured for transferring the observed distress data, the environmental data, the operational data and the modeling data to the controller; a distress rank module coupled to the memory configured to receive the observed distress data and generate a distress rank output value associated with the observed distress data; a predictive module coupled to the distress rank module and to the memory, configured to receive the environmental data and the operational data and the modeling data as parameters from the memory and generate a predictive model of a failure of the physical component using the parameters, the predictive model providing a distress output value; and a comparative module in communication with the distress rank module and the predictive module and configured to: (i) compare the distress output value to the distress rank output value to output a mathematical difference, the distressed output value being scaled before being compared to the distress rank output value; and (ii) communicate the mathematical difference to the predictive module; wherein the predictive module is further configured to tune the predictive model by adjusting at least one parameter of the parameters and generate an updated predictive model providing an updated distress output value, the updated distress output value being generated as a computational model; wherein the updated predictive model is generated when the mathematical difference fails to satisfy a predetermined threshold; and wherein distress of the physical component is predicted using one of the predictive model and the updated predictive model.

18. The system of claim 17 , wherein the predictive module can further reformulate the parameters when the difference between the distress output and the distress rank is not within the threshold.

19. The system of claim 18 , wherein reformulating the parameters is based on a different cumulative damage model of the modeling data.

20. The system of claim 17 , wherein distress rank module can further determine the distress rank output value of the physical component based on a first set of distress data or a second set of distress data accessed from the memory.

21. The system of claim 17 , wherein the controller can further apply the updated predictive model to a design change related to the physical component.